Joint Estimation of State of Charge and State of Health of Lithium-Ion Batteries Based on Stacking Machine Learning Algorithm

Abstract

Conducting online estimation studies of the SOH of lithium-ion batteries is indispensable for extending the cycle life of energy storage batteries. Data-driven methods are efficient, accurate, and do not depend on accurate battery models, which is an important direction for battery state estimation research. However, the relationships between variables in lithium-ion battery datasets are mostly nonlinear, and a single data-driven algorithm is susceptible to a weak generalization ability affected by the dataset itself. Meanwhile, most of the related studies on battery health estimation are offline estimation, and the inability for online estimation is also a problem to be solved. In this study, an integrated learning method based on a stacking algorithm is proposed. In this study, the end voltage and discharge temperature were selected as the characteristics based on the sample data of NASA batteries, and the B0005 battery was used as the training set. After training on the dataset and parameter optimization using a Bayesian algorithm, the trained model was used to predict the SOH of B0007 and B0018 models. After comparative analysis, it was found that the prediction results obtained based on the proposed model not only have high accuracy and a short running time, but also have a strong generalization ability, which has a great potential to achieve online estimation.

Advancements in Battery Management Systems for Electric Vehicles: A MATLAB-Based Simulation of 4S3P Lithium-Ion Battery Packs

“…Despite the notable progress in integrating computational intelligence into battery management systems (BMS), there remains a significant disparity in the holistic adoption of these advanced techniques within a unified BMS framework. The literature reveals persistent challenges in real-time data processing and the predictive accuracy of these systems under variable operational conditions, which are crucial for ensuring the reliability and efficiency of lithium-ion batteries [13][14][15][16].…”

“…By incorporating a suite of machine learning techniques including regression models, neural networks, and decision trees, our framework leverages continuous learning from diverse data sources-such as temperature, voltage, and usage patterns-to dynamically predict and manage battery performance. This leads to a marked improvement in prediction accuracy for SOC and SOH, crucial for optimizing charging cycles and extending battery life [13][14][15][16].…”